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Record W4400980455 · doi:10.1504/ijmbs.2024.140102

Bordering non-citizenship assemblage through migrant legibility: a conceptual framework for tracing hidden forms of legal and bureaucratic violence

2024· article· en· W4400980455 on OpenAlexaffabout
Lindsay Larios, Rupaleem Bhuyan, Catherine Schmidt, Heather Bergen

Bibliographic record

VenueInternational Journal of Migration and Border Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsYork UniversityUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsLegibilityCitizenshipBureaucracyAssemblage (archaeology)Conceptual frameworkTracingSociologyPolitical sciencePsychologyCriminologySocial psychologyLawGeographySocial scienceComputer scienceArtVisual artsPoliticsArchaeology

Abstract

fetched live from OpenAlex

In this paper, we conceptualise migrant legibility as a bordering practice where migrants seeking to maintain status or transition to permanent residency in Canada must negotiate the dynamic milieu of: 1) laws and regulations governing immigrant inclusion; 2) bureaucratic processes for verifying eligibility and admissibility; 3) informal social networks which can expand or restrict access to information and resources. Using two case studies from empirical research with migrants in Canada, we attend to the legal, bureaucratic, and social processes through which migrants must prove their humanity (i.e., biopolitical life) in the context of unpredictable, heterogeneous, multi-scalar, and often hidden forms of legal and bureaucratic violence. Through theorising the legal and bureaucratic violence of legibility, this paper illustrates the historical, political, and economic conditions through which migrant illegality and patterns of imperial/colonial/racial/gendered ordering operate in tandem with neoliberal multicultural constructions of equality and inclusion of autonomous and self-sufficient individuals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.402
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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